Does (mis)communication mitigate the upshot of diversity?

This paper contributes to the literature on how diversity impacts groups by exploring how communication mediates the ability of diverse individuals to work together. To do so we incorporate a communication channel into a representative model of problem-solving by teams of diverse agents that provides the foundations for one of the most widely cited analytical results in the literature on diversity and team performance: the “Diversity Trumps Ability Theorem”. We extend the model to account for the fact that communication between agents is a necessary feature of team problem-solving, and we introduce the possibility that this communication occurs with error, and that this error might sometimes be correlated with how different agents are from one another. Accounting for communication does not give us reason to reject the claim associated with the theorem, that functionally diverse teams tend to outperform more homogeneous teams (even when the homogeneous teams are comprised of individuals with more task relevant expertise). However, incorporating communication into our model clarifies the role that four factors play in moderating the extent to which teams capture the benefits of functional diversity: i) the complexity of the problem, ii) the number of available approaches to solving the problem, iii) the ways of encoding or conceptualizing a problem, and iv) institutional characteristics, such as how teams work together. Specifically, we find that whether (and to what extent) teams capture the benefits of functional diversity depends on how these four factors interact with one another. Particularly important is the role institutional dynamics (like search methods) play in moderating or amplifying interpersonal frictions (like miscommunication), and notably we find that institutions that work in one setting can be counterproductive in other settings.


Major concerns
1. On page 4, the authors relate the DTA theorem to the class of N K models, and introduce the fundamentals of the N K framework. On page 9, the describe that they extend the Hong-Page model. How is the N K framework related to the Hong-Page model? I think that the PLOS One readership with not be familiar with these models, and therefore, the authors should carefully discuss and compare the model (in particular since the DTA theorem is proven for the N K framework). What are the characteristics of landscapes generated using the two frameworks? In which respects to they differ?
2. I think that the model description provided in the paper is not very structures, and in consequence, it is very hard to follow. I highly recommend the authors to consult structured frameworks to introduce agent-based models (such as the ODD or the ODD+D protocol). Even if the authors decide not to follow these frameworks, they might get an idea of how to properly structure a model description and what information should be provided. For example, (a) it is not clear what exactly is the agents' task. Do they explore the entire landscape and share information about the observed topology?
To the jointly perform a task, divide the landscape into interdependent sub-landscapes (or sub-tasks) and the solution implemented in one time step is a a function of all agents' actions? In the first case, how sensitive are the results to the number of agents, i.e., can the global optimum be achieved more often and/or faster when more agents operate on the landscape?
(b) in Sec. 2.1, the authors discuss different approaches to measure diversity/similarity. How diversity/similarity is implemented, is not introduced. Usually, descriptions of agent-based models include descriptions of agents and their characteristics. It would perhaps be an option to introduce agents and their characteristics first, and then, second, discuss how to measure diversity/similarity. For example, as far as I understand, there are some implicit assumptions included in the model: (i) agents are diverse in their search strategies only, (ii) agents have the cognitive capacities to explore the entire landscape, (iii) it does not make a difference whether agents move 1 or 20 positions to explore the landscape (in terms of costs or effort), etc. I think that it is the authors job to make all assumptions explicit.
(c) In Sec. 2.2, the authors introduce their concepts of miscommunication. I think that it is required to properly introduce a model of information exchange first. How is information conceptualized? In the N K framework, locations of peaks are indicated by bitstrings.
How is this piece of information implemented in the model introduced in this paper? Is it an index between 1 and 20,000? Again, as mentioned before, I suggest the authors to carefully compare the N K framework and their model.
(d) In Sec. 2.3, the authors introduce three modes of coordination. There is a lot of research on coordination in teams (that also employs the N K framework). This body of research analyzes the efficiency of coordination mechanisms in different settings (e.g., different task complexity, different patterns of interactions). How do the modes considered in this paper related to this body of research? For example, it is well known that sequential problem solving methods are particularly suited when the interactions (that shape the problem's complexity) follow a hierarchical pattern). How is this finding reflected in the results presented here?
(e) In Sec. 2.4, the authors introduce how their landscapes are generated. The section is called "Other parameters". Isn't it the case the landscapes are a crucial building block of the model? From a logical point of view, I would consider introducing the building blocks of the model in the following order: (i) landscapes on which agents operate, (ii) agents and their characteristics, (iii) structure of interactions between agents, (iv) modes of information exchange. Usually, in agent-based models, agents are dynamic, i.e., they learn. How is learning included in this model? Are the agents' search strategies fixed during one simulation run or do the agents discover new search strategies?
(f) I think that the authors should consider adding a table that includes all model parameters and all values for these parameters considered in the simulation experiments.
(g) at the end of Sec. 2, the authors claim that the results are representative. Usually, I would expect a sensitivity analysis that proves this claim.
(h) How did the authors fix the number of simulation runs? What is the observation period (for how many time steps did the agents search on the landscape)? There are methods to fix the number of simulation runs and time steps described in the literature on simulation modelling, such as using the coefficient of variation to fix these values.
3. The authors discuss some related literature in Secs. 1.1 and 1.2. However, the discussion is very narrow in its focus. For example, a review of the effects of team diversity on group performance (that not explicitly mentions the DTA theorem) might allow to place the results in a wider context.